• Louis S Levene, Richard Baker, John Bankart, Nicola Walker, and Andrew Wilson.
• Department of Health Sciences, University of Leicester, Leicester.
• Br J Gen Pract. 2019 Aug 1; 69 (685): e546e554e546-e554.

BackgroundA previous study found that variables related to population health needs were poor predictors of cross-sectional variations in practice payments.AimTo investigate whether deprivation scores predicted variations in the increase over time of total payments to general practices per patient, after adjustment for potential confounders.Design And SettingLongitudinal multilevel model for 2013-2017; 6900 practices (84.4% of English practices).MethodPractices were excluded if total adjusted payments per patient were <£10 or >£500 per patient or if deprivation scores were missing. Main outcome measures were adjusted total NHS payments; calculated by dividing total NHS payments, after deductions and premises payments, by the number of registered patients in each practice. A total of 17 independent variables relating to practice population and organisational factors were included in the model after checking for collinearity.ResultsAfter adjustment for confounders and the logarithmic transformation of the dependent and main independent variables (due to extremely skewed [positive] distribution of payments), practice deprivation scores predicted very weakly longitudinal variations in total payments' slopes. For each 10% increase in the Index of Multiple Deprivation score, practice payments increased by only 0.06%. The large sample size probably explains why eight of the 17 confounders were significant predictors, but with very small coefficients. Most of the variability was at practice level (intraclass correlation = 0.81).ConclusionThe existing NHS practice payment formula has demonstrated very little redistributive potential and is unlikely to substantially narrow funding gaps between practices with differing workloads caused by the impact of deprivation.© British Journal of General Practice 2019.

16 keywords...

hide…